Overview of current multiparametric magnetic resonance imaging approach in the diagnosis and staging of prostate cancer

@article{Aydin2015OverviewOC,
  title={Overview of current multiparametric magnetic resonance imaging approach in the diagnosis and staging of prostate cancer},
  author={Hasan Riza Aydin and Volkan Kızılg{\"o}z and Berna Okudan Tekin},
  journal={The Kaohsiung Journal of Medical Sciences},
  year={2015},
  volume={31}
}

Role of apparent diffusion coefficient values in prostate diseases characterization on diffusion-weighted magnetic resonance imaging.

ADC values allow differentiation between normal and pathological prostate tissue and between indolent and clinically significant PCa but do not allow a definite differentiation between PCa, HG-PIN, and ASAP.

Decomposition of DCE-MRI Data for the Classification of Benign and Malignant Prostates

Nonnegative matrix factorization (NMF) can be used to analyze the components in DCE-MRI data of the prostate and shows that the characteristic curve with the earliest peak rises rapidly and declines rapidly can beused to classify the malignant and benign prostates accurately.

Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram

The established nomogram combining a biparametric MRI-based radiomics signature and PI-RADS could be utilized for noninvasive and accurate prediction of CS-PCa.

Effect of domain knowledge encoding in CNN model architecture—a prostate cancer study using mpMRI images

The results fail to demonstrate that PI-RADS-based modelling of CNN architecture can significantly improve performance of prostate cancer recognition using mpMRI.

Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit.

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